# A tibble: 6 × 6,701
Diet Source Donor CollectionMet Sex OTU0 OTU1 OTU2 OTU3
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 0 0 0 0 0 1.56e-11 4.72e-11 1.23e-11 4.52e-11
2 0 1 0 0 0 2.36e-11 9.53e-11 3.33e-11 2.67e-11
3 0 2 0 1 0 6.77e-11 3.68e-11 8.02e-11 5.49e-11
4 0 2 0 0 0 5.52e-11 9.89e-11 4.58e-11 3.54e-11
5 0 3 0 0 0 5.24e-11 6.34e-11 2.35e-11 7.47e-11
6 0 4 0 1 0 7.67e-11 7.22e-11 5.41e-11 1.20e-11
# ℹ 6,692 more variables: OTU4 <dbl>, OTU5 <dbl>, OTU6 <dbl>, OTU7 <dbl>,
# OTU8 <dbl>, OTU9 <dbl>, OTU10 <dbl>, OTU11 <dbl>, OTU12 <dbl>, OTU13 <dbl>,
# OTU14 <dbl>, OTU15 <dbl>, OTU16 <dbl>, OTU17 <dbl>, OTU18 <dbl>,
# OTU19 <dbl>, OTU20 <dbl>, OTU21 <dbl>, OTU22 <dbl>, OTU23 <dbl>,
# OTU24 <dbl>, OTU25 <dbl>, OTU26 <dbl>, OTU27 <dbl>, OTU28 <dbl>,
# OTU29 <dbl>, OTU30 <dbl>, OTU31 <dbl>, OTU32 <dbl>, OTU33 <dbl>,
# OTU34 <dbl>, OTU35 <dbl>, OTU36 <dbl>, OTU37 <dbl>, OTU38 <dbl>, …Impact of Dietary Shifts on Gut Microbiome Dynamics
Multivariate Insights Using R
R for Bio Data Analysis
2024-12-03
Introduction
Our aim:
To study the relationship between the composition of the gut microbiota and factors such as diet and colonisation history.
General Workflow
MICROBIOME METADATA:
OTU TAXONOMY GLOSSARY:
OTU.ID Kingdom Phylum Class Order
1 OTU0 Bacteria
2 OTU1 Bacteria Firmicutes Clostridia Clostridiales
3 OTU2 Bacteria Firmicutes Bacilli Lactobacillales
4 OTU3 Bacteria Bacteroidetes Bacteroidetes Bacteroidales
5 OTU4 Bacteria Bacteroidetes
6 OTU5 Bacteria Firmicutes Clostridia Clostridiales
Family Genus X X.1
1
2 Ruminococcaceae
3 Enterococcaceae Enterococcus
4 Porphyromonadaceae Parabacteroides
5
6 Data Tidying and Filtering
Added a SampleID column to uniquely identify each sample.
Transformed the dataset from wide to long format for easier analysis.
Keeping OTUs contributing up to 95% of cumulative abundance.
Replaced the numeric codes with descriptive labels.
# Creation and relocation of SampleID
metadata_df <- metadata_df |>
mutate(SampleID = row_number()) |> # Create SampleID from the first column
relocate(SampleID,
.before = everything()) # Move SampleID to the first position
metadata_df_long <- metadata_df |>
pivot_longer(
cols = starts_with("OTU"),
names_to = "OTU",
values_to = "rel_abundance"
)
head(metadata_df_long)
# Calculate cumulative contribution
cumulative_otus <- metadata_df_long |>
group_by(OTU) |>
summarize(mean_abundance = mean(rel_abundance)) |>
arrange(desc(mean_abundance)) |>
mutate(cumulative_abundance = cumsum(mean_abundance) / sum(mean_abundance))
# Filter OTUs contributing to 95% cumulative abundance
otus_to_keep <- cumulative_otus |>
filter(cumulative_abundance <= 0.95) |>
pull(OTU)
# Number of OTUs before filtering
n_total_otus <- metadata_df_long |>
pull(OTU) |>
n_distinct()
# Number of OTUs after filtering
n_filtered_otus <- filtered_metadata |>
pull(OTU) |>
n_distinct()
filtered_metadata_stricter_label <- filtered_metadata_stricter |>
mutate(Diet = case_when(Diet == 0 ~ "LFPP",
Diet == 1 ~ "Western",
Diet == 2 ~ "CARBR",
Diet == 3 ~ "FATR",
Diet == 4 ~ "Suckling",
Diet == 5 ~ "Human")) |>
mutate(Source = case_when(Source == 0 ~ "Cecum1",
Source == 1 ~ "Cecum2",
Source == 2 ~ "Colon1",
Source == 3 ~ "Colon2",
Source == 4 ~ "Feces",
Source == 5 ~ "SI1",
Source == 6 ~ "SI13",
Source == 7 ~ "SI15",
Source == 8 ~ "SI2",
Source == 9 ~ "SI5",
Source == 10 ~ "SI9",
Source == 11 ~ "Stomach",
Source == 12 ~ "Cecum")) |>
mutate(Donor = case_when(Donor == 0 ~ "HMouseLFPP",
Donor == 1 ~ "CONVR",
Donor == 2 ~ "Human",
Donor == 3 ~ "Fresh",
Donor == 4 ~ "Frozen",
Donor == 5 ~ "HMouseWestern",
Donor == 6 ~ "CONVD")) |>
mutate(CollectionMet = case_when(CollectionMet == 0 ~ "Contents",
CollectionMet == 1 ~ "Scraping")) |>
mutate(Sex = case_when(Sex == 0 ~ "Male",
Sex == 1 ~ "Female"))
head(filtered_metadata_stricter_label)
Microbiota composition in terms of phyla in different:
sources and diet types
![]()
diet and donor combination
![]()
Principal Component Analysis on Phylum-Level Aggregated Microbiome Data
# A tibble: 6 × 9
SampleID Diet Actinobacteria Bacteroidetes Firmicutes Proteobacteria TM7
<dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 339 1 0.00316 0.463 0.470 0.00671 7.70e-11
2 340 1 0.0180 0.106 0.811 0.00138 3.72e-11
3 341 1 0.0121 0.0443 0.873 0.00792 5.68e-11
4 342 1 0.00531 0.145 0.786 0.0135 7.89e-12
5 343 1 0.00801 0.607 0.300 0.0300 3.90e-11
6 344 1 0.631 0.199 0.141 0.00467 3.98e-11
# ℹ 2 more variables: Unclassified <dbl>, Verrucomicrobia <dbl>
Principal Component Analysis on Phylum-Level Aggregated Microbiome Data
Analysis of Microbiome Clusters by Donor Groups Using Hierarchical Clustering
We compute a distance matrix using the Euclidean distance to measure similarity between microbiome samples. The clustering is performed using Ward’s method, which minimizes variance within clusters.
Chi-Squared Test
- Cluster 1 is dominated by HMouseLFPP (55.5%) with notable contributions from Frozen (17.8%) and Fresh (18.7%), reflecting plant-rich diets and preserved samples.
- Cluster 2 includes mostly Fresh (55.1%) and HMouseLFPP (26.5%), indicating a mix of human-derived and dietary influences.
- Cluster 3 is almost entirely CONVR (95%), representing natural microbiota from control mice.
- The chi-squared test confirms significant associations between donor origins and clusters, highlighting the influence of donors on microbiota composition.
Biodiversity and diet
Shannon diversity index
- Number of species living in a habitat (richness)
- Relative abundance (evenness).
\[ H' = -\sum_{i=1}^R p_i \ln p_i \\ p_i \text{ is the relative abundance of OTU}_i\\ R \text{ is the total number of OTUs} \]
Biodiversity in the microbiota of first-generation humanized mice was found to differ significantly across different diets.